diff --git a/ProcessLib/HeatTransportBHE/Tests.cmake b/ProcessLib/HeatTransportBHE/Tests.cmake
index 9f3e7b34f65..2a50eae93ac 100644
--- a/ProcessLib/HeatTransportBHE/Tests.cmake
+++ b/ProcessLib/HeatTransportBHE/Tests.cmake
@@ -94,7 +94,7 @@ AddTest(
     REQUIREMENTS OGS_USE_PYTHON AND NOT OGS_USE_MPI
     DIFF_DATA
     3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_soil temperature_soil 1e-12 1e-13
-    3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_BHE1 temperature_BHE1 1e-12 1e-14
-    3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_BHE2 temperature_BHE2 1e-12 1e-14
-    3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_BHE3 temperature_BHE3 1e-12 1e-14
+    3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_BHE1 temperature_BHE1 1e-10 1e-13
+    3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_BHE2 temperature_BHE2 1e-10 1e-13
+    3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu 3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu temperature_BHE3 temperature_BHE3 1e-10 1e-13
 )
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu b/Tests/Data/Parabolic/T/3D_3BHEs_array/3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu
index f46716f74dc..335b7bd4818 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/3bhes_1U_pcs_0_ts_10_t_7200.000000.vtu
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b287cb24862ca7a802c67dfb071f26efc252baf1d5ca0bedacb979c28d1f6741
-size 220384
+oid sha256:4bd2a3d9539ae71dc3c3008e6f29d9158b91b7e9793dab544a8ffda4bb27100b
+size 220372
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py
index 3efa453eaaa..1c353af5f76 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py
@@ -1,5 +1,5 @@
 ###
-# Copyright (c) 2012-2019, OpenGeoSys Community (http://www.opengeosys.org)
+# Copyright(c) 2012 - 2019, OpenGeoSys Community(http://www.opengeosys.org)
 # Distributed under a Modified BSD License.
 # See accompanying file LICENSE.txt or
 # http://www.opengeosys.org/project/license
@@ -9,185 +9,213 @@
 print(sys.version)
 import os
 import numpy as np
-import pandas as pd
+from pandas import read_csv
 import OpenGeoSys
-from tespy import  cmp, con, nwk, hlp, cmp_char
+from tespy import cmp, con, nwk, hlp, cmp_char
 from tespy import nwkr
 
-###User setting++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-#parameters
-#refrigerant parameters
-refrig_density = 992.92 #kg/m3
-# %% switch for special boundary conditions
-switch_dyn_demand = 'on'# 'on','off', switch of the function for dynamic thermal demand from consumer
-switch_dyn_frate = 'off'# 'on','off', switch of the function for dynamic flowrate in BHE
+# User setting +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+# parameters
+# refrigerant parameters
+refrig_density = 992.92  # kg/m3
+# switch for special boundary conditions
+# 'on','off', switch of the function for dynamic thermal demand from consumer
+switch_dyn_demand = 'on'
+# 'on','off', switch of the function for dynamic flowrate in BHE
+switch_dyn_frate = 'off'
 
-# %% timecurve setting
+
+# timecurve setting
 def timerange(t):
-    #month for closed network
-    timerange_nw_off_month = [-9999]#No month for closed network
+    # month for closed network
+    timerange_nw_off_month = [-9999]  # No month for closed network
     nw_status = 'on'
-    t_trans = int((t-1)/86400/30) + 1#t-1 to avoid the calculation problem at special time point,e.g. t = 2592000.
+    # t-1 to avoid the calculation problem at special time point,
+    # e.g. t = 2592000.
+    t_trans = int((t - 1) / 86400 / 30) + 1
     t_trans_month = t_trans
     if t_trans_month > 12:
-        t_trans_month = t_trans - 12*(int(t_trans/12))
+        t_trans_month = t_trans - 12 * (int(t_trans / 12))
     if t_trans_month in timerange_nw_off_month:
         nw_status = 'off'
     return t_trans, t_trans_month, nw_status
 
-# %% consumer thermal load
-#month demand
-def consumer_demand(t):#dynamic thermal demand from consumer
-    #thermal demand in each month, assumed specific heat extraction rate * lenth of BHE * number of BHE
-    month_demand = [-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3,-25*50*3]
-    return month_demand[t -1]
-
-# %% dynamic hydraulic flow rate
-#month demand
-def dyn_frate(t):#dynamic flowrate in BHE
-    #flow rate in kg/s time curve in month
+
+# consumer thermal load
+# month demand
+def consumer_demand(t):  # dynamic thermal demand from consumer
+    # thermal demand in each month (assumed specific heat extraction rate*
+    # length of BHE* number of BHE)
+    month_demand = [
+        -25 * 50 * 3, -25 * 50 * 3, -25 * 50 * 3, -25 * 50 * 3, -25 * 50 * 3,
+        -25 * 50 * 3, -25 * 50 * 3, -25 * 50 * 3, -25 * 50 * 3, -25 * 50 * 3,
+        -25 * 50 * 3, -25 * 50 * 3
+    ]
+    return month_demand[t - 1]
+
+
+# dynamic hydraulic flow rate
+# month demand
+def dyn_frate(t):  # dynamic flowrate in BHE
+    # flow rate in kg / s time curve in month
     month_frate = [-9999]
-    return month_frate[t -1]
-###End User setting+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    return month_frate[t - 1]
+
+
+# End User setting+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
 
-# %% create network dataframe
+# create network dataframe
 def create_dataframe():
-    #return dataframe
-    df_nw = pd.read_csv('./pre/bhe_network.csv', delimiter=';',
-        index_col=[0], dtype={'data_index':str})
-    return(df_nw)
+    # return dataframe
+    df_nw = read_csv('./pre/bhe_network.csv',
+                        delimiter=';',
+                        index_col=[0],
+                        dtype={'data_index': str})
+    return (df_nw)
 
-# %% TESPy hydraulic calculation process
+
+# TESPy hydraulic calculation process
 def get_hydraulics(t_trans):
-    #if network exist dynamic flowrate
+    # if network exist dynamic flowrate
     if switch_dyn_frate == 'on':
         cur_frate = dyn_frate(t_trans)
-        localVars['con_inflow'].set_attr( m = cur_frate)
-    #solve imported network
+        localVars['inlet_name'].set_attr(m=cur_frate)
+    # solve imported network
     nw.solve(mode='design')
-    #get flowrate #kg/s
+    # get flowrate #kg / s
     for i in range(n_BHE):
         for c in nw.conns.index:
-            if c.t.label == data_index[i]:#t:inlet comp, s:outlet comp
-                df.loc[df.index[i],'flowrate'] = c.get_attr('m').val_SI
-    #convert flowrate to velocity: #m^3/s
+            if c.t.label == data_index[i]:  # t:inlet comp, s:outlet comp
+                df.loc[df.index[i], 'flowrate'] = c.get_attr('m').val_SI
+    # convert flowrate to velocity : #m ^ 3 / s
     for i in range(n_BHE):
-        df.loc[df.index[i],'f_velocity'] = df.loc[df.index[i],'flowrate']/refrig_density
+        df.loc[df.index[i],
+               'f_velocity'] = df.loc[df.index[i], 'flowrate'] / refrig_density
     return df
 
-# %% TESPy Thermal calculation process
+
+# TESPy Thermal calculation process
 def get_thermal(t):
-    #bhe network thermal re parametrization
+    # bhe network thermal re parametrization
     if switch_dyn_demand == 'on':
-        #consumer thermal load:
+        # consumer thermal load:
         cur_month_demand = consumer_demand(t)
-        #print('cur_month_demand',cur_month_demand)
-        nw.busses[bus_name].set_attr(P= cur_month_demand)
-    #T_out:
+        # print('cur_month_demand', cur_month_demand)
+        nw.busses[bus_name].set_attr(P=cur_month_demand)
+    # T_out:
     for i in range(n_BHE):
-        localVars['outlet_BHE'+ str(i+1)].set_attr( T= df.loc[data_index[i],'Tout_val'])
-        #print('Tout=',df.loc[data_index[i],'Tout_val'])
+        localVars['outlet_BHE' + str(i + 1)].set_attr(T=df.loc[data_index[i],
+                                                               'Tout_val'])
+    # print('Tout=', df.loc[data_index[i], 'Tout_val'])
     # solving network
     nw.solve(mode='design')
-    #get Tin_val
+    # get Tin_val
     for i in range(n_BHE):
-        df.loc[df.index[i],'Tin_val'] = localVars['inlet_BHE'+ str(i+1)].get_attr('T').val_SI
-        #print('Tin=',df.loc[df.index[i],'Tin_val'])
+        df.loc[df.index[i],
+               'Tin_val'] = localVars['inlet_BHE' +
+                                      str(i + 1)].get_attr('T').val_SI
+    # print('Tin=', df.loc[df.index[i], 'Tin_val'])
     return df['Tin_val'].tolist()
 
-# %% OGS setting
+
+# OGS setting
 # Dirichlet BCs
 class BC(OpenGeoSys.BHENetwork):
     def initializeDataContainer(self):
-        #convert dataframe to column list
-        t = 0#'initial time'
-        data_col_1 = df['Tin_val'].tolist()#'Tin_val'
-        data_col_2 = df['Tout_val'].tolist()#'Tout_val'
-        data_col_3 = df['Tout_node_id'].astype(int).tolist()#'Tout_node_id'
+        # convert dataframe to column list
+        t = 0  # 'initial time'
+        data_col_1 = df['Tin_val'].tolist()  # 'Tin_val'
+        data_col_2 = df['Tout_val'].tolist()  # 'Tout_val'
+        data_col_3 = df['Tout_node_id'].astype(int).tolist()  # 'Tout_node_id'
         get_hydraulics(0)
-        data_col_4 = df['f_velocity'].tolist()#'BHE flow rate'
-        return (True, t, data_col_1,data_col_2,data_col_3, data_col_4)
+        data_col_4 = df['f_velocity'].tolist()  # 'BHE flow rate'
+        return (t, data_col_1, data_col_2, data_col_3, data_col_4)
+
     def tespyThermalSolver(self, t, Tin_val, Tout_val):
-        #current time, network status:
+        # current time, network status:
         t_trans, t_trans_month, nw_status = timerange(t)
-        #if network closed:
-        #print('nw_status = ', nw_status)
+        # if network closed:
+        #     print('nw_status = ', nw_status)
         if nw_status == 'off':
             return (True, True, Tout_val)
-        #if network works:
         else:
-            #read Tout_val to dataframe
+            # read Tout_val to dataframe
             for i in range(n_BHE):
-                df.loc[df.index[i],'Tout_val'] = Tout_val[i]
-            #TESPy solver
+                df.loc[df.index[i], 'Tout_val'] = Tout_val[i]
+            # TESPy solver
             cur_cal_Tin_val = get_thermal(t_trans_month)
-            #check norm if network achieves the converge
+            # check norm if network achieves the converge
             if_success = False
             pre_cal_Tin_val = Tin_val
-            norm = np.linalg.norm(abs(np.asarray(pre_cal_Tin_val)-np.asarray(cur_cal_Tin_val)))
+            norm = np.linalg.norm(
+                abs(np.asarray(pre_cal_Tin_val) - np.asarray(cur_cal_Tin_val)))
             if norm < 10e-6:
                 if_success = True
-            #return to OGS
+            # return to OGS
             return (True, if_success, cur_cal_Tin_val)
+
     def tespyHydroSolver(self, t):
         if_dyn_frate = False
-        data_f_velocity = df['f_velocity'].tolist()#'f_velocity'
+        data_f_velocity = df['f_velocity'].tolist()
         if switch_dyn_frate == 'on':
             if_dyn_frate = True
-            #current time, network status:
+            # current time, network status:
             t_trans, t_trans_month, nw_status = timerange(t)
             if nw_status == 'off':
                 for i in range(n_BHE):
-                    df.loc[df.index[i],'f_velocity'] = 0
-                data_f_velocity = df['f_velocity'].tolist()#'f_velocity'
+                    df.loc[df.index[i], 'f_velocity'] = 0
+                data_f_velocity = df['f_velocity'].tolist()
             else:
                 dataframe = get_hydraulics(t_trans)
-                data_f_velocity = dataframe['f_velocity'].tolist()#'f_velocity'
-        #return to OGS
+                data_f_velocity = dataframe['f_velocity'].tolist()
+        # return to OGS
         return (if_dyn_frate, data_f_velocity)
 
 
-# %% main
-#initialize the tespy model of the bhe network
-#load path of network model:
-#loading the TESPy model
+# main
+# initialize the tespy model of the bhe network
+# load path of network model:
+# loading the TESPy model
 project_dir = os.getcwd()
 print("Project dir is: ", project_dir)
 nw = nwkr.load_nwk('./pre/tespy_nw')
-#set if print the information of the network
+# set if print the information of the network
 nw.set_printoptions(print_level='none')
 
-#create bhe dataframe of the network system from bhe_network.csv
+# create bhe dataframe of the network system from bhe_network.csv
 df = create_dataframe()
-n_BHE = np.size(df.iloc[:,0])
-#bhes name
-data_index = df.index.tolist()
+n_BHE = np.size(df.iloc[:, 0])
 
-#create local variables of the components label and connections label in network
+# create local variables of the components label and connections label in
+# network
 localVars = locals()
 data_index = df.index.tolist()
 for i in range(n_BHE):
     for c in nw.conns.index:
-        #bhe inlet and outlet conns
-        if c.t.label == data_index[i]:# inlet conns of bhe
-            localVars['inlet_BHE'+ str(i+1)] = c
-        if c.s.label == data_index[i]:# outlet conns of bhe
-            localVars['outlet_BHE'+ str(i+1)] = c
+        # bhe inlet and outlet conns
+        if c.t.label == data_index[i]:  # inlet conns of bhe
+            localVars['inlet_BHE' + str(i + 1)] = c
+        if c.s.label == data_index[i]:  # outlet conns of bhe
+            localVars['outlet_BHE' + str(i + 1)] = c
 
-#time depended consumer thermal demand
+# time depended consumer thermal demand
 if switch_dyn_demand == 'on':
-    #name of bus in the network
-    bus_name = 'consumer heat demand'
-    assert 'consumer heat demand' in bus_name, "bus name should be named with 'consumer heat demand'"
+    # import the name of bus from the network csv file
+    bus_name = read_csv('./pre/tespy_nw/comps/bus.csv',
+                    delimiter=';',
+                    index_col=[0]).index[0]
 
-#time depended flowrate
+# time depended flowrate
 if switch_dyn_frate == 'on':
-    con_infow = 'from consumer inflow'
-    assert 'from consumer inflow' in con_infow, "con_infow should be named with 'from consumer inflow'"
+    # import the name of inlet connection from the network csv file
+    inlet_name = read_csv('./pre/tespy_nw/conn.csv',
+                    delimiter=';',
+                    index_col=[0]).iloc[0,0]
     for c in nw.conns.index:
-        #bhe inflow conns
-        if c.s.label == con_infow:# inlet conns of bhe
-            localVars['con_inflow'] = c
+        # bhe inflow conns
+        if c.s.label == inlet_name:  # inlet conns of bhe
+            localVars['inlet_name'] = c
 
 # instantiate BC objects referenced in OpenGeoSys
-bc_bhe = BC()
\ No newline at end of file
+bc_bhe = BC()
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py
index 2e53ca4d52b..8a35660de64 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py
@@ -5,24 +5,30 @@
 # http://www.opengeosys.org/project/license
 ###
 
+# Execute this file to generate TESPy network csv files
 from tespy import cmp, con, nwk, hlp
 from tespy import nwkr
-from sub_btes import btes_para as bp
-
 import numpy as np
 import pandas as pd
-# %% network
-btes = nwk.network(fluids=['water'], T_unit='K', p_unit='bar', h_unit='kJ / kg', T_range=[273.25, 373.15], p_range=[1, 20], h_range=[1, 1000])
 
-#components
+# %% network
+btes = nwk.network(fluids=['water'],
+                   T_unit='K',
+                   p_unit='bar',
+                   h_unit='kJ / kg',
+                   T_range=[273.25, 373.15],
+                   p_range=[1, 20],
+                   h_range=[1, 1000])
+
+# components
 fc_in = cmp.source('from consumer inflow')
 fc_out = cmp.sink('from consumer outflow')
 
 pu = cmp.pump('pump')
 
-sp = cmp.splitter('splitter',num_out = 3)
+sp = cmp.splitter('splitter', num_out=3)
 
-#bhe:
+# bhe:
 bhe_name = 'BHE1'
 assert 'BHE1' in bhe_name, "BHE should be named with 'BHE1'"
 bhe1 = cmp.heat_exchanger_simple(bhe_name)
@@ -33,11 +39,11 @@
 assert 'BHE3' in bhe_name, "BHE should be named with 'BHE3'"
 bhe3 = cmp.heat_exchanger_simple(bhe_name)
 
-mg = cmp.merge('merge',num_in = 3)
+mg = cmp.merge('merge', num_in=3)
 
 cons = cmp.heat_exchanger_simple('consumer')
 
-#connections
+# connections
 # inlet
 fc_pu = con.connection(fc_in, 'out1', pu, 'in1')
 
@@ -55,10 +61,8 @@
 
 cons_fc = con.connection(cons, 'out1', fc_out, 'in1')
 
-btes.add_conns(fc_pu, pu_sp,
-sp_bhe1, sp_bhe2, sp_bhe3,
-bhe1_mg, bhe2_mg, bhe3_mg,
-mg_cons, cons_fc)
+btes.add_conns(fc_pu, pu_sp, sp_bhe1, sp_bhe2, sp_bhe3, bhe1_mg, bhe2_mg,
+               bhe3_mg, mg_cons, cons_fc)
 
 # busses
 heat = con.bus('consumer heat demand')
@@ -67,62 +71,61 @@
 
 # flow_char
 # provide volumetric flow in m^3 / s
-x = np.array([0.00, 0.001952885971862, 0.00390577194372, 0.005858657915586,
-  0.007811543887448, 0.00976442985931, 0.011717315831173,
-  0.013670201803035, 0.015623087774897, 0.017575973746759,
-  0.019528859718621, 0.021481745690483, 0.023434631662345,
-  0.025387517634207, 0.027340403606069, 0.029293289577931,
-  0.031246175549793, 0.033199061521655, 0.035151947493517,
-  0.037104833465379, 0.039057719437241, 0.041010605409104,
-  0.042963491380966, 0.044916377352828, 0.04686926332469,
-  0.048822149296552, 0.050775035268414, 0.052727921240276,
-  0.054680807212138, 0.056633693184
-  ])
+x = np.array([
+    0.00, 0.00001952885971862, 0.00390577194372, 0.005858657915586,
+    0.007811543887448, 0.00976442985931, 0.011717315831173, 0.013670201803035,
+    0.015623087774897, 0.017575973746759, 0.019528859718621, 0.021481745690483,
+    0.023434631662345, 0.025387517634207, 0.027340403606069, 0.029293289577931,
+    0.031246175549793, 0.033199061521655, 0.035151947493517, 0.037104833465379,
+    0.039057719437241, 0.041010605409104, 0.042963491380966, 0.044916377352828,
+    0.04686926332469, 0.048822149296552, 0.050775035268414, 0.052727921240276,
+    0.054680807212138, 0.056633693184
+])
 
 # provide head in Pa
-y = np.array([0.47782539, 0.47725723, 0.47555274, 0.47271192, 0.46873478,
-  0.46362130, 0.45737151, 0.44998538, 0.44146293, 0.43180416,
-  0.4220905, 0.40907762, 0.39600986, 0.38180578, 0.36646537,
-  0.34998863, 0.33237557, 0.31362618, 0.29374046, 0.27271841,
-  0.25056004, 0.22726535, 0.20283432, 0.17726697, 0.15056329,
-  0.12272329, 0.09374696, 0.06363430, 0.03238531, 0.00000000
-  ])*1e5
+y = np.array([
+    0.47782539, 0.47725723, 0.47555274, 0.47271192, 0.46873478, 0.46362130,
+    0.45737151, 0.44998538, 0.44146293, 0.43180416, 0.4220905, 0.40907762,
+    0.39600986, 0.38180578, 0.36646537, 0.34998863, 0.33237557, 0.31362618,
+    0.29374046, 0.27271841, 0.25056004, 0.22726535, 0.20283432, 0.17726697,
+    0.15056329, 0.12272329, 0.09374696, 0.06363430, 0.03238531, 0.00000000
+]) * 1e5
 
 f = hlp.dc_cc(x=x, y=y, is_set=True)
-pu.set_attr( flow_char=f)
+pu.set_attr(flow_char=f)
 
-#components paramerization
+# components paramerization
 # system inlet
-inflow_head = 2 #bar
+inflow_head = 2  # bar
 
-fc_pu.set_attr(p=inflow_head, m = 0.6, fluid={'water': 1})
+fc_pu.set_attr(p=inflow_head, m=0.6, fluid={'water': 1})
 
-#pump
-pu.set_attr(eta_s = 0.90)
+# pump
+pu.set_attr(eta_s=0.90)
 
-#bhes
-bhe1.set_attr(D=0.02733,L=100,ks=0.001)
-bhe2.set_attr(D=0.02733,L=100,ks=0.001)
-bhe3.set_attr(D=0.02733,L=100,ks=0.001)
+# bhes
+bhe1.set_attr(D=0.02733, L=100, ks=0.00001)
+bhe2.set_attr(D=0.02733, L=100, ks=0.00001)
+bhe3.set_attr(D=0.02733, L=100, ks=0.00001)
 
-#consumer
-cons.set_attr(D=0.2,L=20,ks=0.001)
+# consumer
+cons.set_attr(D=0.2, L=20, ks=0.00001)
 
-##connection parametrization
-#Tin:
-pu_sp.set_attr(h= con.ref(cons_fc, 1, 0))
+# connection parametrization
+# Tin:
+pu_sp.set_attr(h=con.ref(cons_fc, 1, 0))
 
-#for BHEs:
-#Tout:
-bhe1_mg.set_attr(T= 303.15)
-bhe2_mg.set_attr(T= 303.15)
-bhe3_mg.set_attr(T= 303.15)
+# for BHEs:
+# Tout:
+bhe1_mg.set_attr(T=303.15)
+bhe2_mg.set_attr(T=303.15)
+bhe3_mg.set_attr(T=303.15)
 
 # consumer heat demand
-heat.set_attr(P=-3000) #W
+heat.set_attr(P=-3000)  # W
 
-#solve
+# solve
 btes.set_printoptions(print_level='info')
 btes.solve('design')
-#save to csv:
-btes.save('tespy_nw', structure=True)
\ No newline at end of file
+# save to csv:
+btes.save('tespy_nw', structure=True)
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/bus.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/bus.csv
index c063591a267..971f617570c 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/bus.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/bus.csv
@@ -1,2 +1,2 @@
 label;P;P_set
-consumer heat demand;-3000.0;True
+consumer heat demand;-3000.0000000000005;True
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/char.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/char.csv
index 27edda66e28..f2aa4423131 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/char.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/char.csv
@@ -1,8 +1,8 @@
 id;x;y
-0x00000256999C5FD0;[0.071, 0.282, 0.635, 0.776, 0.917, 1.0, 1.128, 1.27, 1.41, 1.763, 2.115, 2.5];[0.25, 0.547, 0.9, 0.965, 0.995, 1.0, 0.99, 0.959, 0.911, 0.737, 0.519, 0.25]
-0x00000256999C5198;[0.0, 0.001952885971862, 0.00390577194372, 0.005858657915586, 0.007811543887448, 0.00976442985931, 0.011717315831173, 0.013670201803035, 0.015623087774897, 0.017575973746759, 0.019528859718621, 0.021481745690483, 0.023434631662345, 0.025387517634207, 0.027340403606069, 0.029293289577931, 0.031246175549793, 0.033199061521655, 0.035151947493517, 0.037104833465379, 0.039057719437241, 0.041010605409104, 0.042963491380966, 0.044916377352828, 0.04686926332469, 0.048822149296552, 0.050775035268414, 0.052727921240276, 0.054680807212138, 0.056633693184];[47782.539000000004, 47725.723, 47555.274, 47271.192, 46873.478, 46362.130000000005, 45737.151, 44998.538, 44146.293, 43180.416, 42209.049999999996, 40907.761999999995, 39600.986, 38180.578, 36646.537000000004, 34998.863000000005, 33237.557, 31362.618, 29374.046, 27271.841000000004, 25056.003999999997, 22726.535, 20283.432, 17726.697, 15056.329, 12272.329, 9374.696, 6363.43, 3238.531, 0.0]
-0x00000256999DE400;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
-0x00000256999DE2E8;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
-0x00000256999DE2B0;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
-0x00000256999DE4E0;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
-0x000002569999C0F0;[0, 1, 2, 3];[1, 1, 1, 1]
+0x000001D4D54B5438;[0.071, 0.282, 0.635, 0.776, 0.917, 1.0, 1.128, 1.27, 1.41, 1.763, 2.115, 2.5];[0.25, 0.547, 0.9, 0.965, 0.995, 1.0, 0.99, 0.959, 0.911, 0.737, 0.519, 0.25]
+0x000001D4D54B54A8;[0.0, 1.952885971862e-05, 0.00390577194372, 0.005858657915586, 0.007811543887448, 0.00976442985931, 0.011717315831173, 0.013670201803035, 0.015623087774897, 0.017575973746759, 0.019528859718621, 0.021481745690483, 0.023434631662345, 0.025387517634207, 0.027340403606069, 0.029293289577931, 0.031246175549793, 0.033199061521655, 0.035151947493517, 0.037104833465379, 0.039057719437241, 0.041010605409104, 0.042963491380966, 0.044916377352828, 0.04686926332469, 0.048822149296552, 0.050775035268414, 0.052727921240276, 0.054680807212138, 0.056633693184];[47782.539000000004, 47725.723, 47555.274, 47271.192, 46873.478, 46362.130000000005, 45737.151, 44998.538, 44146.293, 43180.416, 42209.049999999996, 40907.761999999995, 39600.986, 38180.578, 36646.537000000004, 34998.863000000005, 33237.557, 31362.618, 29374.046, 27271.841000000004, 25056.003999999997, 22726.535, 20283.432, 17726.697, 15056.329, 12272.329, 9374.696, 6363.43, 3238.531, 0.0]
+0x000001D4D54B57B8;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
+0x000001D4D54B56A0;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
+0x000001D4D54B5668;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
+0x000001D4D54B5898;[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.5, 2.0];[0.03, 0.158, 0.344, 0.469, 0.535, 0.59, 0.638, 0.68, 0.718, 0.752, 0.783, 0.812, 0.839, 0.864, 0.887, 0.909, 0.929, 0.948, 0.966, 0.984, 1.0, 1.128, 1.216]
+0x000001D4D547E358;[0, 1, 2, 3];[1, 1, 1, 1]
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/heat_exchanger_simple.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/heat_exchanger_simple.csv
index d98ac9c3c47..9cdb9d8e63d 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/heat_exchanger_simple.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/heat_exchanger_simple.csv
@@ -1,5 +1,5 @@
-label;cp;busses;bus_param;bus_P_ref;bus_char;mode;design;offdesign;interface;Q;Q_set;Q_var;pr;pr_set;pr_var;zeta;zeta_set;zeta_var;D;D_set;D_var;L;L_set;L_var;ks;ks_set;ks_var;kA;kA_set;kA_var;Tamb;Tamb_set;Tamb_var;kA_char;kA_char_set;kA_char_method;kA_char_param;SQ1;SQ1_set;SQ2;SQ2_set;Sirr;Sirr_set;hydro_group;kA_group
-BHE1;heat_exchanger_simple;[];[];[];[];auto;[];[];False;1000.0000000000027;False;False;0.944519712897267;False;False;422220855.5384383;False;False;0.02733;True;False;100;True;False;0.001;True;False;1;False;False;1;False;False;0x00000256999DE400;False;HE_HOT;m;3.314363668686166;False;nan;False;nan;False;default;default
-BHE2;heat_exchanger_simple;[];[];[];[];auto;[];[];False;1000.0000000000027;False;False;0.944519712897267;False;False;422220855.5384383;False;False;0.02733;True;False;100;True;False;0.001;True;False;1;False;False;1;False;False;0x00000256999DE2E8;False;HE_HOT;m;3.314363668686166;False;nan;False;nan;False;default;default
-BHE3;heat_exchanger_simple;[];[];[];[];auto;[];[];False;1000.0000000000028;False;False;0.944519712897267;False;False;422220855.53843814;False;False;0.02733;True;False;100;True;False;0.001;True;False;1;False;False;1;False;False;0x00000256999DE2B0;False;HE_HOT;m;3.3143636686861666;False;nan;False;nan;False;default;default
-consumer;heat_exchanger_simple;['consumer heat demand'];['P'];[-3000.0];['0x000002569999C0F0'];auto;[];[];False;-3000.0;False;False;0.99999701435903;False;False;2384.5451514143924;False;False;0.2;True;False;20;True;False;0.001;True;False;1;False;False;1;False;False;0x00000256999DE4E0;False;HE_HOT;m;-9.915667365778233;False;nan;False;nan;False;default;default
+label;cp;busses;bus_param;bus_P_ref;bus_char;design;offdesign;interface;design_path;Q;Q_set;Q_var;pr;pr_set;pr_var;zeta;zeta_set;zeta_var;D;D_set;D_var;L;L_set;L_var;ks;ks_set;ks_var;kA;kA_set;kA_var;Tamb;Tamb_set;Tamb_var;kA_char;kA_char_set;kA_char_method;kA_char_param;SQ1;SQ1_set;SQ2;SQ2_set;Sirr;Sirr_set;hydro_group;kA_group
+BHE1;heat_exchanger_simple;[];[];[];[];[];[];False;nan;999.9999999999999;False;False;0.9736837578540255;False;False;200221979.33309928;False;False;0.02733;True;False;100;True;False;1e-05;True;False;1;False;False;1;False;False;0x000001D4D54B57B8;False;HE_HOT;m;3.3095575754670445;False;nan;False;nan;False;default;default
+BHE2;heat_exchanger_simple;[];[];[];[];[];[];False;nan;999.9999999999998;False;False;0.9736837578540255;False;False;200221979.33309937;False;False;0.02733;True;False;100;True;False;1e-05;True;False;1;False;False;1;False;False;0x000001D4D54B56A0;False;HE_HOT;m;3.309557575467044;False;nan;False;nan;False;default;default
+BHE3;heat_exchanger_simple;[];[];[];[];[];[];False;nan;999.9999999999999;False;False;0.9736837578540255;False;False;200221979.33309928;False;False;0.02733;True;False;100;True;False;1e-05;True;False;1;False;False;1;False;False;0x000001D4D54B5668;False;HE_HOT;m;3.3095575754670445;False;nan;False;nan;False;default;default
+consumer;heat_exchanger_simple;['consumer heat demand'];['P'];[-3000.0000000000005];['0x000001D4D547E358'];[];[];False;nan;-3000.0000000000005;False;False;0.9999971030369322;False;False;2384.545016264847;False;False;0.2;True;False;20;True;False;1e-05;True;False;1;False;False;1;False;False;0x000001D4D54B5898;False;HE_HOT;m;-9.915667457524025;False;nan;False;nan;False;default;default
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/merge.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/merge.csv
index f2c3f2cba8b..cee506f2668 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/merge.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/merge.csv
@@ -1,2 +1,2 @@
-label;cp;busses;bus_param;bus_P_ref;bus_char;mode;design;offdesign;interface;num_in;num_in_set;zero_flag;zero_flag_set
-merge;merge;[];[];[];[];auto;[];[];False;3;True;nan;False
+label;cp;busses;bus_param;bus_P_ref;bus_char;design;offdesign;interface;design_path;num_in;num_in_set;zero_flag;zero_flag_set
+merge;merge;[];[];[];[];[];[];False;nan;3;True;nan;False
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/pump.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/pump.csv
index 39180c9268e..05198eaf46f 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/pump.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/pump.csv
@@ -1,2 +1,2 @@
-label;cp;busses;bus_param;bus_P_ref;bus_char;mode;design;offdesign;interface;P;P_set;P_var;eta_s;eta_s_set;eta_s_var;pr;pr_set;pr_var;Sirr;Sirr_set;eta_s_char;eta_s_char_set;eta_s_char_method;eta_s_char_param;flow_char;flow_char_set;flow_char_method;flow_char_param
-pump;pump;[];[];[];[];auto;[];[];False;31.969279414546325;False;False;0.899999999964523;True;False;1.238825068833766;False;False;0.01058760490432178;False;0x00000256999C5FD0;False;GENERIC;nan;0x00000256999C5198;True;default;nan
+label;cp;busses;bus_param;bus_P_ref;bus_char;design;offdesign;interface;design_path;P;P_set;P_var;eta_s;eta_s_set;eta_s_var;pr;pr_set;pr_var;eta_s_char;eta_s_char_set;eta_s_char_method;eta_s_char_param;flow_char;flow_char_set;flow_char_method;flow_char_param;Sirr;Sirr_set
+pump;pump;[];[];[];[];[];[];False;nan;31.925887420863727;False;False;0.9000000008463441;True;False;1.2385007970539827;False;False;0x000001D4D54B5438;False;GENERIC;nan;0x000001D4D54B54A8;True;default;nan;0.010573179069774596;False
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/sink.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/sink.csv
index 6dbfd94be87..a474db522d6 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/sink.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/sink.csv
@@ -1,2 +1,2 @@
-label;cp;busses;bus_param;bus_P_ref;bus_char;mode;design;offdesign;interface
-from consumer outflow;sink;[];[];[];[];auto;[];[];False
+label;cp;busses;bus_param;bus_P_ref;bus_char;design;offdesign;interface;design_path
+from consumer outflow;sink;[];[];[];[];[];[];False;nan
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/source.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/source.csv
index b235a300b92..567787d0718 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/source.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/source.csv
@@ -1,2 +1,2 @@
-label;cp;busses;bus_param;bus_P_ref;bus_char;mode;design;offdesign;interface
-from consumer inflow;source;[];[];[];[];auto;[];[];False
+label;cp;busses;bus_param;bus_P_ref;bus_char;design;offdesign;interface;design_path
+from consumer inflow;source;[];[];[];[];[];[];False;nan
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/splitter.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/splitter.csv
index 9fae5099eec..a97cf46f7b8 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/splitter.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/comps/splitter.csv
@@ -1,2 +1,2 @@
-label;cp;busses;bus_param;bus_P_ref;bus_char;mode;design;offdesign;interface;num_out;num_out_set
-splitter;splitter;[];[];[];[];auto;[];[];False;3;True
+label;cp;busses;bus_param;bus_P_ref;bus_char;design;offdesign;interface;design_path;num_out;num_out_set
+splitter;splitter;[];[];[];[];[];[];False;nan;3;True
diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/conn.csv b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/conn.csv
index b9952a9a0b7..09bbabb137a 100644
--- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/conn.csv
+++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/tespy_nw/conn.csv
@@ -1,11 +1,11 @@
-id;s;s_id;t;t_id;design;offdesign;m;m_unit;m_unit_set;m0;m_set;m_ref;m_ref_f;m_ref_d;m_ref_set;p;p_unit;p_unit_set;p0;p_set;p_ref;p_ref_f;p_ref_d;p_ref_set;h;h_unit;h_unit_set;h0;h_set;h_ref;h_ref_f;h_ref_d;h_ref_set;T;T_unit;T_unit_set;T0;T_set;T_ref;T_ref_f;T_ref_d;T_ref_set;x;x_unit;x_unit_set;x0;x_set;x_ref;x_ref_f;x_ref_d;x_ref_set;v;v_unit;v_unit_set;v0;v_set;v_ref;v_ref_f;v_ref_d;v_ref_set;Td_bp;Td_bp_unit;Td_bp_unit_set;Td_bp0;Td_bp_set;Td_bp_ref;Td_bp_ref_f;Td_bp_ref_d;Td_bp_ref_set;state;state_set;water;water0;water_set;balance
-0x0000025699994940;from consumer inflow;out1;pump;in1;[];[];0.6;kg / s;False;0.6;True;nan;nan;nan;False;2.0;bar;False;2.0;True;nan;nan;nan;False;120.89024042914106;kJ / kg;False;120.89024042914106;False;nan;nan;nan;False;301.948409703941;K;False;301.948409703941;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006023792974044687;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;True;False
-0x0000025699994A90;pump;out1;splitter;in1;[];[];0.6;kg / s;False;0.6;False;nan;nan;nan;False;2.477650137667532;bar;False;2.477650137667532;False;nan;nan;nan;False;120.94352256149864;kJ / kg;False;120.94352256149864;False;0x000002569999CC50;1.0;0.0;True;301.95069870488595;K;False;301.95069870488595;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006023667951052704;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x0000025699994CC0;splitter;out1;BHE1;in1;[];[];0.19999999999999996;kg / s;False;0.19999999999999996;False;nan;nan;nan;False;2.477650137667532;bar;False;2.477650137667532;False;nan;nan;nan;False;120.94352256149864;kJ / kg;False;120.94352256149864;False;nan;nan;nan;False;301.95069870488595;K;False;301.95069870488595;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020078893170175676;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x0000025699994EF0;splitter;out2;BHE2;in1;[];[];0.19999999999999996;kg / s;False;0.19999999999999996;False;nan;nan;nan;False;2.477650137667532;bar;False;2.477650137667532;False;nan;nan;nan;False;120.94352256149864;kJ / kg;False;120.94352256149864;False;nan;nan;nan;False;301.95069870488595;K;False;301.95069870488595;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020078893170175676;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x0000025699994A20;splitter;out3;BHE3;in1;[];[];0.19999999999999998;kg / s;False;0.19999999999999998;False;nan;nan;nan;False;2.477650137667532;bar;False;2.477650137667532;False;nan;nan;nan;False;120.94352256149864;kJ / kg;False;120.94352256149864;False;nan;nan;nan;False;301.95069870488595;K;False;301.95069870488595;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0002007889317017568;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x000002569999C390;BHE1;out1;merge;in1;[];[];0.19999999999999998;kg / s;False;0.19999999999999998;False;nan;nan;nan;False;2.3401893966896115;bar;False;2.3401893966896115;False;nan;nan;nan;False;125.94352256149865;kJ / kg;False;125.94352256149865;False;nan;nan;nan;False;303.1500000000442;K;False;303.1500000000442;True;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020086198047386772;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x000002569999C5C0;BHE2;out1;merge;in2;[];[];0.19999999999999998;kg / s;False;0.19999999999999998;False;nan;nan;nan;False;2.3401893966896115;bar;False;2.3401893966896115;False;nan;nan;nan;False;125.94352256149865;kJ / kg;False;125.94352256149865;False;nan;nan;nan;False;303.1500000000442;K;False;303.1500000000442;True;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020086198047386772;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x000002569999C7F0;BHE3;out1;merge;in3;[];[];0.2;kg / s;False;0.2;False;nan;nan;nan;False;2.3401893966896115;bar;False;2.3401893966896115;False;nan;nan;nan;False;125.94352256149865;kJ / kg;False;125.94352256149865;False;nan;nan;nan;False;303.1500000000442;K;False;303.1500000000442;True;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020086198047386775;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x000002569999CA20;merge;out1;consumer;in1;[];[];0.6;kg / s;False;0.6;False;nan;nan;nan;False;2.3401893966896115;bar;False;2.3401893966896115;False;nan;nan;nan;False;125.94352256149864;kJ / kg;False;125.94352256149864;False;nan;nan;nan;False;303.1500000000442;K;False;303.1500000000442;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006025859414216032;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
-0x000002569999CC50;consumer;out1;from consumer outflow;in1;[];[];0.6;kg / s;False;0.6;False;nan;nan;nan;False;2.340182409724271;bar;False;2.340182409724271;False;nan;nan;nan;False;120.94352256149864;kJ / kg;False;120.94352256149864;False;nan;nan;nan;False;301.95370869039704;K;False;301.95370869039704;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006023710401413591;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+id;s;s_id;t;t_id;design;offdesign;design_path;m;m_unit;m_unit_set;m0;m_set;m_ref;m_ref_f;m_ref_d;m_ref_set;p;p_unit;p_unit_set;p0;p_set;p_ref;p_ref_f;p_ref_d;p_ref_set;h;h_unit;h_unit_set;h0;h_set;h_ref;h_ref_f;h_ref_d;h_ref_set;T;T_unit;T_unit_set;T0;T_set;T_ref;T_ref_f;T_ref_d;T_ref_set;x;x_unit;x_unit_set;x0;x_set;x_ref;x_ref_f;x_ref_d;x_ref_set;v;v_unit;v_unit_set;v0;v_set;v_ref;v_ref_f;v_ref_d;v_ref_set;Td_bp;Td_bp_unit;Td_bp_unit_set;Td_bp0;Td_bp_set;Td_bp_ref;Td_bp_ref_f;Td_bp_ref_d;Td_bp_ref_set;state;state_set;water;water0;water_set;balance
+0x000001D4D546ACC0;from consumer inflow;out1;pump;in1;[];[];nan;0.6;kg / s;False;0.6;True;nan;nan;nan;False;2.0;bar;False;2.0;True;nan;nan;nan;False;120.89684447187135;kJ / kg;False;120.89684447187135;False;nan;nan;nan;False;301.94998968965183;K;False;301.94998968965183;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006023795760273974;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;True;False
+0x000001D4D546AE10;pump;out1;splitter;in1;[];[];nan;0.6;kg / s;False;0.6;False;nan;nan;nan;False;2.4770015941079655;bar;False;2.4770015941079655;False;nan;nan;nan;False;120.95005428423946;kJ / kg;False;120.95005428423946;False;0x000001D4D5470F98;1.0;0.0;True;301.95227563825125;K;False;301.95227563825125;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006023670907654493;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D546AA58;splitter;out1;BHE1;in1;[];[];nan;0.19999999999999998;kg / s;False;0.19999999999999998;False;nan;nan;nan;False;2.4770015941079655;bar;False;2.4770015941079655;False;nan;nan;nan;False;120.95005428423946;kJ / kg;False;120.95005428423946;False;nan;nan;nan;False;301.95227563825125;K;False;301.95227563825125;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020078903025514975;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D5470278;splitter;out2;BHE2;in1;[];[];nan;0.19999999999999996;kg / s;False;0.19999999999999996;False;nan;nan;nan;False;2.4770015941079655;bar;False;2.4770015941079655;False;nan;nan;nan;False;120.95005428423946;kJ / kg;False;120.95005428423946;False;nan;nan;nan;False;301.95227563825125;K;False;301.95227563825125;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020078903025514972;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D54704A8;splitter;out3;BHE3;in1;[];[];nan;0.19999999999999998;kg / s;False;0.19999999999999998;False;nan;nan;nan;False;2.4770015941079655;bar;False;2.4770015941079655;False;nan;nan;nan;False;120.95005428423946;kJ / kg;False;120.95005428423946;False;nan;nan;nan;False;301.95227563825125;K;False;301.95227563825125;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020078903025514975;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D54706D8;BHE1;out1;merge;in1;[];[];nan;0.2;kg / s;False;0.2;False;nan;nan;nan;False;2.4118162203614557;bar;False;2.4118162203614557;False;nan;nan;nan;False;125.95005428423946;kJ / kg;False;125.95005428423946;False;nan;nan;nan;False;303.14999999999895;K;False;303.14999999999895;True;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0002008613366014869;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D5470908;BHE2;out1;merge;in2;[];[];nan;0.19999999999999998;kg / s;False;0.19999999999999998;False;nan;nan;nan;False;2.4118162203614557;bar;False;2.4118162203614557;False;nan;nan;nan;False;125.95005428423946;kJ / kg;False;125.95005428423946;False;nan;nan;nan;False;303.14999999999895;K;False;303.14999999999895;True;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.00020086133660148688;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D5470B38;BHE3;out1;merge;in3;[];[];nan;0.2;kg / s;False;0.2;False;nan;nan;nan;False;2.4118162203614557;bar;False;2.4118162203614557;False;nan;nan;nan;False;125.95005428423946;kJ / kg;False;125.95005428423946;False;nan;nan;nan;False;303.14999999999895;K;False;303.14999999999895;True;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0002008613366014869;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D5470D68;merge;out1;consumer;in1;[];[];nan;0.6000000000000001;kg / s;False;0.6000000000000001;False;nan;nan;nan;False;2.4118162203614557;bar;False;2.4118162203614557;False;nan;nan;nan;False;125.95005428423946;kJ / kg;False;125.95005428423946;False;nan;nan;nan;False;303.14999999999895;K;False;303.14999999999895;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006025840098044607;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
+0x000001D4D5470F98;consumer;out1;from consumer outflow;in1;[];[];nan;0.6000000000000001;kg / s;False;0.6000000000000001;False;nan;nan;nan;False;2.411809233418939;bar;False;2.411809233418939;False;nan;nan;nan;False;120.95005428423946;kJ / kg;False;120.95005428423946;False;nan;nan;nan;False;301.95370308274016;K;False;301.95370308274016;False;nan;nan;nan;False;-1.0;nan;False;nan;False;nan;nan;nan;False;0.0006023691039076023;m3 / s;False;nan;False;nan;nan;nan;False;nan;K;False;nan;False;nan;nan;nan;False;nan;False;1;1;False;False
diff --git a/web/content/docs/benchmarks/heat-transport-bhe/3D_3BHEs_array.pandoc b/web/content/docs/benchmarks/heat-transport-bhe/3D_3BHEs_array.pandoc
index 92d399c65a0..6f77637c453 100644
--- a/web/content/docs/benchmarks/heat-transport-bhe/3D_3BHEs_array.pandoc
+++ b/web/content/docs/benchmarks/heat-transport-bhe/3D_3BHEs_array.pandoc
@@ -40,7 +40,7 @@ The BHE used in this Model contains a single U-shape pipe (1U type). The details
 | Circulating fluid heat capacity        | $(\rho c)_{f}$     | $4.16\times10^{6}$  | $\mathrm{J m^{-3}K^{-1}}$   |
 | Circulating fluid flow rate            | $\mathbf{u}$       | $0.0002$            | $\mathrm{m^{3} s^{-1}}$     |
 | Length of the BHE U-pipe in network    | $l$                | $100$               | $\mathrm{m}$                |
-| Roughness coefficient of the pipe      | $k_s$              | $0.001$             | $\mathrm{m}$                |
+| Roughness coefficient of the pipe      | $k_s$              | $0.00001$           | $\mathrm{m}$                |
 
 TESPy {#TESPy .unnumbered .unnumbered}
 =====